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local SoftPlus, parent = torch.class('nn.SoftPlus', 'nn.Module')
function SoftPlus:__init(beta)
parent.__init(self)
self.beta = beta or 1 -- Beta controls sharpness of transfer function
self.threshold = 20 -- Avoid floating point issues with exp(x), x>20
end
function SoftPlus:updateOutput(input)
-- f(x) = 1/beta * log(1 + exp(beta * x))
input.THNN.SoftPlus_updateOutput(
input:cdata(),
self.output:cdata(),
self.beta,
self.threshold
)
return self.output
end
function SoftPlus:updateGradInput(input, gradOutput)
-- d/dx[log(1+exp(k*x))/k] = exp(kx) / (exp(kx) + 1)
-- SINCE
-- y = (1/k)*log(1+exp(k*x)) --> x = (1/k)*log(exp(k*y)-1)
-- THEREFORE:
-- d/dx(f(x)) = (exp(k*y) - 1) / exp(k*y)
input.THNN.SoftPlus_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.output:cdata(),
self.beta,
self.threshold
)
return self.gradInput
end
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